Hello,

I am working on an experiment trying different implementations of hybrid quantum neuronal networks: I have a larger classical Neuronal network and replace the final dense layer with different quantum layers.

So far, I tried the pennylane basic- and strong entanglement layer templates as well as a self-developed circuit. All of these worked very fine and generated good results. Now tried implementations of the CVNeuralNetLayers described in Killoran et.al. 2018. ( https://pennylane.readthedocs.io/en/stable/code/api/pennylane.CVNeuralNetLayers.html )

However, using this layer I run into wired Tensorflow errors when trying to train the model:

**tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute Mul as input #1(zero-based) was expected to be a float tensor but is a double tensor [Op:Mul]**

Does anyone know what this error means or at least how I can avoid this?

Best wishes,

Philipp

Here my Implementation

A) The Qnode

```
import pennylane as qml
from pennylane import numpy as np
def get_CVQuantumLayer( num_inputs, num_outputs, n_layers=1, device="strawberryfields.fock" , cutoff_dim=10):
n_qubits = num_inputs
if num_outputs>n_qubits:
raise ValueError("Number of output measurements can not be larger then number of Q-Bits")
# 0) Initialisations
# 0.1) define device
dev = qml.device( device , wires=n_qubits , cutoff_dim=cutoff_dim )
# 0.2) Define weight shapes (following Killoran et.al. 2018 )
L = n_layers
M = n_qubits
K = int( M/2*(M-1) )
weight_shapes = {'theta_1':(L,K), 'phi_1':(L,K), 'varphi_1':(L,M), 'r':(L,M), 'phi_r':(L,M), 'theta_2':(L,K), 'phi_2': (L,K), 'varphi_2':(L,M), 'a':(L,M), 'phi_a':(L,M), 'k':(L,M) }
# 1) Define QNode to build quantum layer
@qml.qnode(dev,interface='tf')
def qnode(inputs, theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k):
# 1.1) Squeezing Embedding
qml.templates.SqueezingEmbedding(inputs, wires=range(n_qubits) )
# 1.2) CVNN-Layers
qml.templates.CVNeuralNetLayers(theta_1, phi_1, varphi_1, r, phi_r, theta_2, phi_2, varphi_2, a, phi_a, k,
wires=[i for i in range(n_qubits)] )
return [qml.expval(qml.NumberOperator(wires=i)) for i in range(num_outputs)]
# 2) make QLayer
qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=num_outputs)
return qlayer
```

B) The Network (simplified version)

```
import tensorflow as tf
input = tf.keras.Input( shape=input_data_size )
layer_dense = tf.keras.layers.Dense(dense_layer_size)(input)
# 1.Q) Generate Quantum Layer as output
# 1.Q.0) Define parameters
num_qubits = 3 # number of Qbits used
num_qlayers= 1 # number of layers in Qlayer.
# 1.Q.1) add additional dense layer to reduce dimension to reduce number of qubits required
layer_quant_in = tf.keras.layers.Dense( num_qubits )(layer_dense)
# 1.Q.2) Load quantum layer
from QLayers import get_CVQuantumLayer
qlayer = get_CVQuantumLayer(num_qubits, output_data_size, num_qlayers )(layer_quant_in)
# 3.2) Assemble Network
model = tf.keras.Model(inputs=input_data, outputs=qlayer)
model.compile(optimizer=tf.optimizers.Adam(), loss=tf.keras.losses.MeanSquaredError())
```